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MLB Batting Splits 2026

Performance splits by handedness, home/away, and situational categories.

Batting splits break down a hitter's performance across different game situations. Platoon splits (vs LHP/RHP) are the most predictive for DFS and prop betting. Minimum 10 at-bats displayed.
Lawrence Butler headshot
Cleveland Indians
.514 OPS
AVG .133
OBP .381
SLG .133
HR 0
AB 15
H 2
RBI 0
Gunnar Henderson headshot
Texas Rangers
.514 OPS
AVG .125
OBP .222
SLG .292
HR 1
AB 24
H 3
RBI 4
Colson Montgomery headshot
Boston Red Sox
.514 OPS
AVG .217
OBP .296
SLG .217
HR 0
AB 23
H 5
RBI 2
Ezequiel Tovar headshot
Miami Marlins
.514 OPS
AVG .174
OBP .167
SLG .348
HR 1
AB 23
H 4
RBI 4
Spencer Horwitz headshot
Cincinnati Reds
.514 OPS
AVG .174
OBP .296
SLG .217
HR 0
AB 23
H 4
RBI 4
Xavier Edwards headshot
San Diego Padres
.513 OPS
AVG .211
OBP .250
SLG .263
HR 0
AB 19
H 4
RBI 0
Curtis Mead headshot
Pittsburgh Pirates
.513 OPS
AVG .211
OBP .250
SLG .263
HR 0
AB 19
H 4
RBI 0
Bryson Stott headshot
Chicago Cubs
.513 OPS
AVG .235
OBP .278
SLG .235
HR 0
AB 17
H 4
RBI 1
Vladimir Guerrero Jr. headshot
Detroit Tigers
.513 OPS
AVG .136
OBP .240
SLG .273
HR 1
AB 22
H 3
RBI 1
Kevin McGonigle headshot
Tampa Bay Rays
.513 OPS
AVG .190
OBP .227
SLG .286
HR 0
AB 21
H 4
RBI 0
Braden Montgomery headshot
Minnesota Twins
.513 OPS
AVG .136
OBP .240
SLG .273
HR 1
AB 22
H 3
RBI 2
Myles Straw headshot
Tampa Bay Rays
.513 OPS
AVG .185
OBP .290
SLG .222
HR 0
AB 27
H 5
RBI 0
Carson Benge headshot
Chicago Cubs
.513 OPS
AVG .185
OBP .290
SLG .222
HR 0
AB 27
H 5
RBI 1
Carlos Cortes headshot
Baltimore Orioles
.513 OPS
AVG .238
OBP .227
SLG .286
HR 0
AB 21
H 5
RBI 2
Mark Vientos headshot
Washington Nationals
.512 OPS
AVG .167
OBP .212
SLG .300
HR 0
AB 30
H 5
RBI 3
Trea Turner headshot
Arizona Diamondbacks
.512 OPS
AVG .160
OBP .192
SLG .320
HR 1
AB 25
H 4
RBI 3
Drake Baldwin headshot
San Francisco Giants
.512 OPS
AVG .160
OBP .192
SLG .320
HR 1
AB 25
H 4
RBI 2
Nick Gonzales headshot
Arizona Diamondbacks
.511 OPS
AVG .238
OBP .273
SLG .238
HR 0
AB 21
H 5
RBI 0
TJ Friedl headshot
Miami Marlins
.511 OPS
AVG .238
OBP .273
SLG .238
HR 0
AB 21
H 5
RBI 0
Ketel Marte headshot
Pittsburgh Pirates
.511 OPS
AVG .185
OBP .214
SLG .296
HR 0
AB 27
H 5
RBI 2
Luis Arraez headshot
New York Mets
.510 OPS
AVG .219
OBP .229
SLG .281
HR 0
AB 32
H 7
RBI 5
Sung-Mun Song headshot
Arizona Diamondbacks
.510 OPS
AVG .190
OBP .320
SLG .190
HR 0
AB 21
H 4
RBI 2
George Springer headshot
Detroit Tigers
.510 OPS
AVG .176
OBP .333
SLG .176
HR 0
AB 17
H 3
RBI 1
Myles Straw headshot
Boston Red Sox
.510 OPS
AVG .188
OBP .229
SLG .281
HR 0
AB 32
H 6
RBI 4
Trent Grisham headshot
Oakland Athletics
.510 OPS
AVG .190
OBP .320
SLG .190
HR 0
AB 21
H 4
RBI 2

Understanding Opponent Splits

Opponent splits reveal how a hitter performs against each MLB team. These splits capture the combined effect of a team's pitching staff, defensive alignment, and park factors. Some hitters consistently dominate certain teams due to favorable pitching matchups.

Team-Specific Matchups

Some hitters own certain teams. This often reflects favorable matchups against that team's pitching staff — handedness advantages, pitch-type weaknesses, or familiarity from division play. Division rivals face each other 13+ times per season, creating larger sample sizes.

Stacking by Opponent

For DFS, opponent splits help identify entire lineups to stack. If multiple hitters on a team have strong splits against today's opponent, that's a high-correlation stack. Combine with the opposing starter's recent form for maximum edge.

Sample Size Caution

Opponent splits against non-division teams can be small (3-4 games per season). Weight division matchups more heavily since they have 13+ games of data. A .400 AVG in 10 at-bats against a team is interesting but not predictive on its own.

Data Source & Methodology

Batting splits sourced from MLB Stats API. Stats reflect current season data and update daily as games are played.

Frequently Asked Questions

How reliable are opponent batting splits?

Division opponent splits are the most reliable since hitters face those teams 13+ times per season. Interleague and non-division splits have smaller sample sizes (3-7 games) and should be weighted less heavily. Always check the at-bat count before drawing conclusions.

How do I use opponent splits for DFS stacks?

Find teams where multiple hitters have strong splits against today's opponent. Stack 3-4 hitters from that team in your DFS lineup for high correlation. This works especially well when the opposing starter is also weak against that lineup's handedness profile.

Why do some hitters crush certain teams?

It usually comes down to pitching staff matchups. A hitter might face favorable pitch types, have platoon advantages against most of a team's rotation, or thrive at that team's home park. Division familiarity also plays a role — hitters see the same pitchers repeatedly.
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